Reduce Operational Costs with Custom AI Automation
AI automation helps small businesses reduce costs by replacing repetitive manual tasks with custom software. It improves efficiency by processing data in seconds instead of minutes, eliminating human error.
Syntora specializes in building custom AI automation systems to help small businesses reduce operational costs. We approach each project as an engineering engagement, designing tailored solutions for critical workflows rather than selling off-the-shelf products.
This approach applies to business-critical workflows that demand reliability, such as processing invoices or qualifying new sales leads. Building such systems requires professional engineering, not a fragile web of point-and-click tools. The scope of an engagement is typically determined by the number of existing systems to integrate and the complexity of the specific business rules involved.
What Problem Does This Solve?
Many businesses try to connect their apps using visual automation platforms. These are great for simple notifications, but they fail when a core business process depends on them. Their pricing models, based on a 'per-task' count, become expensive quickly. A workflow that reads an email, extracts an attachment, calls an AI model, and updates a CRM record can burn through 4 tasks for a single item.
A regional insurance agency with 6 adjusters faced this issue. They tried to automate claim intake from emails. The workflow needed to parse the PDF form, look up the policyholder in their CRM, and create a new record in their claims system. The platform's API connector would time out on policies with long histories, causing the entire workflow to fail silently. About 15% of claims never made it into the system, forcing a daily manual reconciliation that defeated the purpose of automation.
These platforms lack essential production features. There is no built-in logic for retrying a failed API call. There is no way to write a custom validation rule, like checking if a policy number matches a specific format. When it breaks, the business process stops, and there is no engineer to call because you are the one who dragged and dropped the boxes.
How Would Syntora Approach This?
Syntora would begin an engagement by auditing your manual workflow and mapping it into a sequence of Python functions. We do not use visual editors; we write custom code. For document processing, a multi-step manual process can be defined as a state machine within a FastAPI application. We would use the Claude API for data extraction from documents, as we've built similar pipelines for financial documents, and its capabilities are well-suited for reliably parsing complex claim forms or applicant resumes.
The system would be engineered for resilience from its foundation. All external API calls, such as connecting to a CRM or an applicant tracking system, would use `httpx`, which incorporates automatic retries with exponential backoff. If an external API is temporarily unavailable, the system would automatically wait and retry the request multiple times before failing. We would use a lightweight database like Supabase to cache common lookups, improving performance for frequently accessed data.
The entire system would be deployed on AWS Lambda. This serverless function architecture allows the code to run efficiently in response to triggers, such as a new document arriving via email or an API call. This setup is highly efficient for event-driven workloads, often resulting in hosting costs under $50 per month for processing thousands of documents. All logs would be sent as structured JSON using `structlog` to AWS CloudWatch, enabling clear tracing of every step within a transaction.
Upon completion, Syntora would deliver the full source code in your company's private GitHub repository. The deliverable is a durable asset: a custom-built system designed to run on your infrastructure, using your API keys, and documented for any future engineer to maintain or extend. A typical engagement, from initial discovery to a deployed production system, usually spans 2 to 4 weeks, depending on complexity.
What Are the Key Benefits?
Launch in 3 Weeks, Not 3 Quarters
From our first call to a deployed production system in 15-20 business days. Your team starts saving time immediately.
One-Time Build Cost, Not Per-Seat SaaS
A fixed-price engagement for the build. Afterwards, you only pay for low-cost cloud hosting, not a recurring software license.
You Get the Keys and the Blueprint
We deliver the complete Python source code to your GitHub repository, including a runbook for operating the system.
Know About Failures in Milliseconds
We configure monitoring with AWS CloudWatch and alerts via Slack. If an external API fails, you know instantly.
Connects Directly to Your Systems
Custom API integrations with your CRM, ERP, and internal databases. We build the connections that off-the-shelf platforms lack.
What Does the Process Look Like?
Week 1: Scoping & System Access
You walk us through the manual process and grant read-only access to the necessary systems. We deliver a technical spec outlining the build.
Weeks 2-3: Core System Development
We write the production code in Python using FastAPI, pushing daily updates to your private GitHub repo. You get a weekly progress summary.
Week 4: Deployment & Integration
We deploy the application to your cloud infrastructure and conduct end-to-end testing. You receive the system documentation and runbook.
Post-Launch: Monitoring & Handoff
For two weeks post-launch, we monitor system performance and stability. Then, we hand over full control with an optional monthly maintenance plan.
Frequently Asked Questions
- How much does a custom AI automation project cost?
- Pricing is based on a fixed project scope. The primary factors are the number of external systems we need to integrate with and the complexity of the business logic. A simple two-system integration, like a document parser that writes to a CRM, is a standard 2-week build. A multi-step process involving conditional logic and three or more systems typically takes 4 weeks. Book a discovery call to discuss your specific process.
- What happens when an integrated service like the Claude API has an outage?
- The system is designed to handle this. API calls are wrapped in retry logic, so temporary outages are handled automatically. For a longer outage, the AWS Lambda function will place the failed job into a dead-letter queue. We receive an immediate alert and can re-process the jobs from the queue once the external service is back online. Your data is never lost.
- How is this different from hiring a freelancer on Upwork?
- We deliver a production-ready system, not just a script. This includes deployment, infrastructure setup, logging, monitoring, and documentation. The person you talk to on the discovery call is the engineer who writes the code and supports the system after launch. There are no project managers or handoffs. You get direct access to the builder from start to finish.
- We don't have an AWS account. Can you still help?
- Yes. We can set up a new, secure AWS account for your business as part of the engagement. We provision all the necessary infrastructure using code, which means your environment is repeatable and documented from day one. You own the account and all the resources in it. We just build and deploy the system there for you.
- Can your systems work with our on-premise, proprietary software?
- If your software has a documented API that is accessible from the internet, yes. We have connected to custom-built SOAP and REST APIs. If there is no API, but there is a database we can connect to (like PostgreSQL or SQL Server), we can often work with that as well. This requires more discovery time but is a common part of our projects.
- What happens if our business process changes after the build?
- Since you own the code, you are free to have any developer modify it. For clients on our flat-rate monthly maintenance plan, we scope minor changes and enhancements as part of that agreement. For major changes, such as adding a new system to integrate with, we would scope it as a new, small fixed-price project.
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